Many manufacturing and service equipment installations today include, in addition to systems for controlling machines and processes, systems for machine condition monitoring. Machine condition monitoring systems include an array of sensors installed on the equipment, a communications network linking those sensors, and a processor connected to the network for receiving signals from the sensors and making determinations on machine conditions from those signals.
The purpose of machine condition monitoring is to detect faults as early as possible to avoid further damage to machines. Traditionally, physical models were employed to describe the relationship between sensors that measure performance of a machine. Violation of those physical relationships could indicate faults. However, accurate physical models are often difficult to acquire.
An alternative to the use of physical models is the use of statistical models based on machine learning techniques. That approach has gained increased interest in recent decades. In contrast to a physical model, which assumes known sensor relationships, a statistical model learns the relationships among sensors from historical data. That characteristic of the statistical models is a big advantage in that the same generic model can be applied to different machines. The learned models differ only in their parameters.
There are two basic types of statistical models used in machine condition monitoring a regression-based model and a classification-based model. In a regression model, a set of sensors are used to predict (or estimate) another sensor. Since a regression model can produce a continuous estimate, the deviation of the actual value from the estimate can be used directly for fault diagnosis. For example, a simple logic can be built as “the larger the deviation, the greater the chance of a fault.”
In a classification-based model, the output is discrete. One application of a classification-based model is an out-of-range detector wherein a one-class classifier is often employed. A one-class classifier output indicates whether there is an out-of-range condition or not. Such output information is too limited to be useful in any sophisticated level of machine fault diagnostics. There, is therefore a need to extract useful information from a one-class classifier to benefit high-level fault diagnosis.
One-class classification refers to a special type of pattern recognition problem. Let C1 be a certain class of interest. For a test input x, a one-class classifier output indicates whether x belongs to C1 or C0 (which represents any class other than C1). If x does not belong to C1, then x is often called an anomaly (or a novelty). Generally, the objective of training a one-class classifier is to find an evaluation function ƒ(x), which indicates the confidence or probability that the input x belongs to C1. That evaluation function ƒ(x) accordingly defines the decision region R1 for class C1 such that R1={x: ƒ(x)≧T}, where T is a decision threshold. If ƒ(x)≧T, x is classified as C1; otherwise, x is classified as C0.
One-class classification has been used in many applications including machine condition monitoring. In many one-class classification problems, only a binary decision output is available; i.e., x belongs to C1 or x belongs to C0. In many circumstances, however, in addition to knowing that x is an anomaly, there is also a need to evaluate that anomaly to see how different it is from the distribution of C1.
A decision region R1, shown in FIG. 1, depicts a normal operating range of a sensor vector in a machine condition monitoring example. The range R1 is bounded by line 120 defined by ƒ(x)=T. During a monitoring period, two different anomalies, x1 and x2, are detected by the same one-class classifier. Of the two, x2 is very different from the normal operating range R1; it is therefore very likely that x2 represents a faulty state. x1, however, is much closer to R1. Such a small deviation may not be due to a fault, but may instead be due to a minor misoperation or measurement noise. In the case of x2, inspection should be called right away. In case of x1, however, typically a warning note should be made, and further observation should be required before any serious action is taken. It would therefore be beneficial to have additional information about a measurement beyond a simple indication whether the measurement is within the normal range.
One could directly use the evaluation function ƒ(x) to evaluate an anomaly. In many algorithms, however, the value of ƒ(x) does not contain physical significance and cannot serve as a meaningful measure.
There is therefore presently a need for a method for providing additional information about measurements in a one-class classification system used in machine condition monitoring. That method should glean information about how different a particular anomaly is from the normal operating range of a machine.